Evaluating AI's mathematical reasoning requires specialized pipelines beyond single-pass LLM judgment—combining multiple verification methods dramatically improves accuracy on research-level problems.
Researchers evaluated AI models and formal tools on the Ramanujan Challenge, a benchmark of research-level number theory problems. They developed an AI-assisted pipeline to reliably judge complex proofs in multiple formats (natural language, code, formal proofs), reducing false positives from 23% to 6% compared to simple LLM evaluation.